| Ecosystem |
| 0 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Paid | Free |
| Capabilities | 8 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Searches across 1M+ hours of indexed podcast, interview, and talk audio content using dual search modes: text-based keyword matching for exact phrase discovery and semantic search for conceptual relevance. Returns segment-level results with speaker identification, precise timestamps (HH:MM:SS format), and relevance scoring (0-1 float). Implements pagination via offset/limit parameters (max 200 results per query) and supports sorting by relevance, publication date, or episode title. Results include direct URLs with timestamp anchors enabling one-click navigation to specific moments in audio.
Unique: Combines speaker identification with dual search modes (text + semantic) across 275,000+ pre-transcribed podcasts, returning segment-level results with precise timestamps and direct playback URLs. Unlike generic audio search, it indexes speaker identity and enables conceptual discovery across a curated corpus of 1M+ hours.
vs alternatives: Faster and more accurate than manual podcast searching or generic web search because it operates on pre-transcribed, indexed audio with speaker metadata rather than requiring real-time transcription or relying on episode descriptions alone.
Lists recently published podcast episodes with configurable lookback window (1-365 days, default 7 days) and optional filtering by specific podcast IDs. Returns structured episode metadata including title, podcast name, publication date (YYYY-MM-DD), duration in seconds, and direct episode URLs. Supports pagination via limit parameter (1-100 episodes per request). Designed as a lightweight alternative to full search for discovering fresh content within a time window.
Unique: Provides lightweight, time-windowed episode listing with optional podcast filtering, enabling efficient discovery of recent content without full-text search overhead. Optimized for agents that need to stay current with specific podcast feeds rather than search across the entire corpus.
vs alternatives: More efficient than running broad searches for recent content because it directly indexes publication dates and returns only new episodes, avoiding the computational cost of semantic or text matching across the full 1M+ hour corpus.
Retrieves complete episode content including full transcript, metadata (title, podcast, publication date, duration), and speaker information for a specified episode ID. Enables downstream processing of full episode context rather than segment-level search results. Implementation details are partially documented; full transcript retrieval mechanism and context window handling are not fully specified in available documentation.
Unique: Provides direct access to full episode transcripts with speaker identification and metadata, enabling AI models to process complete episode context rather than isolated search segments. Integrates with Audioscrape's 99.2% transcription accuracy and speaker identification pipeline.
vs alternatives: More efficient than downloading raw audio and running local transcription because it returns pre-transcribed, speaker-identified content with timestamps, saving compute time and enabling immediate downstream processing.
Exposes Audioscrape's audio search and retrieval capabilities as standardized MCP (Model Context Protocol) tools, enabling Claude, other LLM-based assistants, and AI agents to call audio search functions natively without custom API integration code. Implements OAuth 2.0 authentication with dynamic client registration following MCP spec 6/18. All tools are read-only (no mutation capabilities). Server endpoint is mcp.audioscrape.com, supporting remote MCP connections from any MCP-compatible client.
Unique: Provides standardized MCP tool bindings for audio search, enabling AI assistants to call Audioscrape functions as native tools without custom API integration. Uses OAuth 2.0 dynamic client registration for secure, user-specific authentication within MCP framework.
vs alternatives: Simpler than building custom API clients because it leverages MCP's standardized tool protocol, allowing Claude and other MCP-compatible assistants to call audio search functions with zero custom integration code. Enables natural language queries to be translated directly to structured audio searches.
Implements tiered subscription plans (Free, Basic, Pro, Enterprise) with explicit monthly quotas for searches, API calls, and transcription minutes. Free plan: 10 searches/month, 50 transcription minutes/month. Basic plan: 50 searches/month, 50 API calls/month, 1000 transcription minutes/month. Pro plan: unlimited searches, 1000 API calls/month, 5000 transcription minutes/month. Enterprise: unlimited access. Rate limiting is enforced server-side at the MCP endpoint; quota consumption is tracked per API key and reset monthly.
Unique: Implements multi-dimensional quota system (searches, API calls, transcription minutes) across four subscription tiers, with monthly reset cycles. Quota enforcement is server-side at the MCP endpoint, preventing quota-aware clients from needing local tracking.
vs alternatives: More transparent than usage-based pricing because quotas are fixed and predictable per plan, enabling builders to estimate costs upfront. Simpler than per-request metering because quota resets monthly rather than requiring real-time billing calculations.
Enables users to upload private audio files (meetings, calls, proprietary recordings) for indexing and search within their own Audioscrape account. Uploaded audio is transcribed, speaker-identified, and indexed using the same pipeline as public podcasts, making it searchable via the standard search_audio_content tool. Private uploads are isolated to the uploading user's account and not visible to other users. Transcription of private audio consumes the user's monthly transcription minute quota.
Unique: Extends Audioscrape's indexing pipeline to user-uploaded private audio, enabling unified search across public podcasts and proprietary content. Private uploads are isolated per user and consume the user's transcription quota, creating a hybrid public/private search experience.
vs alternatives: More integrated than managing separate transcription and search systems because private uploads use the same indexing and search infrastructure as public podcasts, enabling single-query search across both sources without custom integration.
Supports filtering search results by podcast IDs, publication date range (date_from/date_to in YYYY-MM-DD format), and recency (last_week, last_month, last_year enum). Sorting options include relevance (default), publication date, and episode title, with ascending or descending order. Filters are applied server-side during search execution, reducing result set before returning to client. Pagination via offset/limit enables iterating through filtered results.
Unique: Provides server-side filtering and sorting across multiple dimensions (podcast, date, recency, relevance), reducing client-side processing and enabling efficient result refinement without fetching full result sets.
vs alternatives: More efficient than client-side filtering because filters are applied at the server during query execution, reducing data transfer and processing latency compared to fetching all results and filtering locally.
Optional include_context parameter in search_audio_content enables retrieval of surrounding audio segments adjacent to matched results, providing narrative context around search hits. When enabled, results include not just the matched segment but also preceding and following segments from the same episode, enabling AI models to understand broader context without requiring full episode retrieval. Context window size is not documented.
Unique: Enables optional retrieval of surrounding segments adjacent to search matches, providing narrative context without requiring full episode transcripts. Reduces latency compared to full episode retrieval while providing more context than isolated segment matches.
vs alternatives: More efficient than full episode retrieval because it returns only relevant segments plus immediate context, reducing data transfer and processing overhead while still providing sufficient context for AI reasoning.
Executes SQL queries against Supabase PostgreSQL instances through the Model Context Protocol, translating natural language or structured query requests into parameterized SQL statements. Uses MCP's tool-calling interface to expose database operations as callable functions with schema validation, enabling LLM agents to perform CRUD operations, joins, and aggregations with automatic connection pooling and credential management through Supabase client SDK.
Unique: Exposes Supabase PostgreSQL as MCP tools with automatic credential injection from Supabase client SDK, eliminating manual connection string management and enabling seamless LLM-to-database queries within Claude or compatible agents
vs alternatives: Tighter integration than generic SQL MCP servers because it leverages Supabase's built-in authentication and connection pooling rather than requiring separate database credential configuration
Exposes Supabase Auth session state and user metadata through MCP tools, allowing agents to inspect current authentication context, retrieve user profiles, and trigger auth-related operations. Integrates with Supabase's JWT-based auth system to validate sessions and access user claims without re-authenticating, using the Supabase client's built-in session management.
Unique: Integrates Supabase's JWT-based auth system directly into MCP tool interface, allowing agents to inspect and act on auth state without managing separate credential stores or re-authentication flows
vs alternatives: More seamless than generic auth MCP servers because it leverages Supabase's built-in session management and avoids redundant credential passing between agent and auth system
Invokes Supabase Edge Functions (serverless TypeScript/JavaScript functions) through MCP tools, passing parameters and receiving results with optional streaming support. Uses Supabase's edge function HTTP API to trigger functions with automatic authentication headers and response parsing, enabling agents to execute custom business logic without embedding it in the agent itself.
Supabase scores higher at 42/100 vs Audioscrape at 23/100. Audioscrape leads on quality, while Supabase is stronger on ecosystem. Supabase also has a free tier, making it more accessible.
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Unique: Exposes Supabase Edge Functions as MCP tools with automatic authentication and response parsing, allowing agents to invoke custom serverless logic without managing HTTP clients or credential injection
vs alternatives: More integrated than generic HTTP MCP tools because it handles Supabase-specific authentication, error handling, and response formatting automatically
Subscribes to real-time changes on Supabase tables through MCP's event streaming interface, using Supabase's PostgreSQL LISTEN/NOTIFY mechanism to push INSERT, UPDATE, and DELETE events to agents. Maintains persistent WebSocket connections and filters events by table and row-level policies, enabling agents to react to database changes without polling.
Unique: Bridges Supabase's PostgreSQL LISTEN/NOTIFY real-time system with MCP's tool interface, enabling agents to subscribe to database changes without managing WebSocket connections or event serialization
vs alternatives: More efficient than polling-based approaches because it uses Supabase's native real-time infrastructure rather than repeated database queries
Manages files in Supabase Storage buckets through MCP tools, supporting upload, download, list, and delete operations with automatic authentication and path-based access control. Uses Supabase's S3-compatible storage API with built-in support for public/private buckets and signed URLs for temporary access, enabling agents to handle file I/O without managing cloud storage credentials.
Unique: Exposes Supabase Storage's S3-compatible API as MCP tools with automatic authentication and signed URL generation, eliminating the need for agents to manage cloud storage credentials or generate temporary access tokens
vs alternatives: More integrated than generic S3 MCP tools because it leverages Supabase's built-in bucket policies and authentication rather than requiring separate AWS credentials
Performs semantic similarity searches on vector embeddings stored in Supabase PostgreSQL using pgvector extension, translating natural language queries into embedding vectors and executing cosine/L2 distance searches. Integrates with embedding providers (OpenAI, Cohere) or uses pre-computed embeddings, enabling agents to retrieve semantically similar documents or records without full-text search limitations.
Unique: Integrates pgvector directly into MCP tools with automatic embedding generation and distance calculation, enabling agents to perform semantic search without managing separate vector database infrastructure
vs alternatives: More efficient than external vector databases (Pinecone, Weaviate) for Supabase users because it colocates embeddings with relational data, reducing network latency and simplifying data synchronization
Exposes Supabase database schema information through MCP tools, allowing agents to discover table structures, column types, constraints, and relationships without manual schema documentation. Queries PostgreSQL information_schema and Supabase metadata tables to dynamically generate schema descriptions, enabling agents to construct valid queries and understand data relationships.
Unique: Queries Supabase's PostgreSQL information_schema directly through MCP tools, enabling agents to dynamically discover and adapt to database schemas without pre-configured schema definitions
vs alternatives: More flexible than static schema definitions because it reflects live database state, including recent migrations or schema changes
Enforces Supabase Row-Level Security policies within agent queries, ensuring that agents can only access rows permitted by RLS rules defined in the database. Evaluates policies based on authenticated user context (JWT claims, user ID) and applies WHERE clause filters automatically, preventing unauthorized data access at the database layer rather than application layer.
Unique: Delegates authorization enforcement to PostgreSQL RLS policies rather than implementing authorization in agent code, ensuring that data access rules are centralized and cannot be bypassed by agent logic
vs alternatives: More secure than application-level authorization because RLS is enforced at the database layer, preventing accidental data leaks even if agent code has bugs
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